```python
from langchain.text_splitter import (
    RecursiveCharacterTextSplitter,
    Language,
)
```


```python
Full list of support languages
[e.value for e in Language]
```

<CodeOutputBlock lang="python">

```
    ['cpp',
     'go',
     'java',
     'js',
     'php',
     'proto',
     'python',
     'rst',
     'ruby',
     'rust',
     'scala',
     'swift',
     'markdown',
     'latex',
     'html',
     'sol',]
```

</CodeOutputBlock>


```python
You can also see the separators used for a given language
RecursiveCharacterTextSplitter.get_separators_for_language(Language.PYTHON)
```

<CodeOutputBlock lang="python">

```
    ['\nclass ', '\ndef ', '\n\tdef ', '\n\n', '\n', ' ', '']
```

</CodeOutputBlock>

## Python

这里是使用 PythonTextSplitter 的示例


```python
PYTHON_CODE = """
def hello_world():
    print("Hello, World!")

# Call the function
hello_world()
"""
python_splitter = RecursiveCharacterTextSplitter.from_language(
    language=Language.PYTHON, chunk_size=50, chunk_overlap=0
)
python_docs = python_splitter.create_documents([PYTHON_CODE])
python_docs
```

<CodeOutputBlock lang="python">

```
    [Document(page_content='def hello_world():\n    print("Hello, World!")', metadata={}),
     Document(page_content='# Call the function\nhello_world()', metadata={})]
```

</CodeOutputBlock>

## JS

这里是使用 JS 文本分割器的示例


```python
JS_CODE = """
function helloWorld() {
  console.log("Hello, World!");
}

// Call the function
helloWorld();
"""

js_splitter = RecursiveCharacterTextSplitter.from_language(
    language=Language.JS, chunk_size=60, chunk_overlap=0
)
js_docs = js_splitter.create_documents([JS_CODE])
js_docs
```

<CodeOutputBlock lang="python">

```
    [Document(page_content='function helloWorld() {\n  console.log("Hello, World!");\n}', metadata={}),
     Document(page_content='// Call the function\nhelloWorld();', metadata={})]
```

</CodeOutputBlock>

## Markdown

这里是使用 Markdown 文本分割器的示例


```python
markdown_text = """
    # 🦜️🔗 LangChain

    ⚡ Building applications with LLMs through composability ⚡

    ## Quick Install

        ``` bash
            pip install langchain
        ```

    As an open source project in a rapidly developing field, we are extremely open to contributions.
"""
```


```python
md_splitter = RecursiveCharacterTextSplitter.from_language(
    language=Language.MARKDOWN, chunk_size=60, chunk_overlap=0
)
md_docs = md_splitter.create_documents([markdown_text])
md_docs
```

<CodeOutputBlock lang="python">

```
    [Document(page_content='# 🦜️🔗 LangChain', metadata={}),
     Document(page_content='⚡ Building applications with LLMs through composability ⚡', metadata={}),
     Document(page_content='## Quick Install', metadata={}),
     Document(page_content="``` bash\n# Hopefully this code block isn't split ", metadata ={}),
     Document(page_content ='pip install langchain', metadata ={}),
     Document(page_content ='```', metadata={}),
     Document(page_content='As an open source project in a rapidly developing field, we', metadata={}),
     Document(page_content='are extremely open to contributions.', metadata={})]
```

</CodeOutputBlock>

## Latex

这里是使用 Latex 文本的示例


```python
latex_text = """
\documentclass{article}

\begin{document}

\maketitle

\section{Introduction}
Large language models (LLMs) are a type of machine learning model that can be trained on vast amounts of text data to generate human-like language. In recent years, LLMs have made significant advances in a variety of natural language processing tasks, including language translation, text generation, and sentiment analysis.

\subsection{History of LLMs}
The earliest LLMs were developed in the 1980s and 1990s, but they were limited by the amount of data that could be processed and the computational power available at the time. In the past decade, however, advances in hardware and software have made it possible to train LLMs on massive datasets, leading to significant improvements in performance.

\subsection{Applications of LLMs}
LLMs have many applications in industry, including chatbots, content creation, and virtual assistants. They can also be used in academia for research in linguistics, psychology, and computational linguistics.

\end{document}
"""
```


```python
latex_splitter = RecursiveCharacterTextSplitter.from_language(
    language=Language.MARKDOWN, chunk_size=60, chunk_overlap=0
)
latex_docs = latex_splitter.create_documents([latex_text])
latex_docs
```

<CodeOutputBlock lang="python">

```
    [Document(page_content='\\documentclass{article}\n\n\x08egin{document}\n\n\\maketitle', metadata={}),
     Document(page_content='\\section{Introduction}', metadata={}),
     Document(page_content='Large language models (LLMs) are a type of machine learning', metadata={}),
     Document(page_content='model that can be trained on vast amounts of text data to', metadata={}),
     Document(page_content='generate human-like language. In recent years, LLMs have', metadata={}),
     Document(page_content='made significant advances in a variety of natural language', metadata={}),
     Document(page_content='processing tasks, including language translation, text', metadata={}),
     Document(page_content='generation, and sentiment analysis.', metadata={}),
     Document(page_content='\\subsection{History of LLMs}', metadata={}),
     Document(page_content='The earliest LLMs were developed in the 1980s and 1990s,', metadata={}),
     Document(page_content='but they were limited by the amount of data that could be', metadata={}),
     Document(page_content='processed and the computational power available at the', metadata={}),
     Document(page_content='time. In the past decade, however, advances in hardware and', metadata={}),
     Document(page_content='software have made it possible to train LLMs on massive', metadata={}),
     Document(page_content='datasets, leading to significant improvements in', metadata={}),
     Document(page_content='performance.', metadata={}),
     Document(page_content='\\subsection{Applications of LLMs}', metadata={}),
     Document(page_content='LLMs have many applications in industry, including', metadata={}),
     Document(page_content='chatbots, content creation, and virtual assistants. They', metadata={}),
     Document(page_content='can also be used in academia for research in linguistics,', metadata={}),
     Document(page_content='psychology, and computational linguistics.', metadata={}),
     Document(page_content='\\end{document}', metadata={})]
```

</CodeOutputBlock>

## HTML

这里是使用 HTML 文本分割器的示例


```python
html_text = """
<!DOCTYPE html>
<html>
    <head>
        <title>🦜️🔗 LangChain</title>
        <style>
            body {
                font-family: Arial, sans-serif;
            }
            h1 {
                color: darkblue;
            }
        </style>
    </head>
    <body>
        <div>
            <h1>🦜️🔗 LangChain</h1>
            <p>⚡ Building applications with LLMs through composability ⚡</p>
        </div>
        <div>
            As an open source project in a rapidly developing field, we are extremely open to contributions.
        </div>
    </body>
</html>
"""
```


```python
html_splitter = RecursiveCharacterTextSplitter.from_language(
    language=Language.MARKDOWN, chunk_size=60, chunk_overlap=0
)
html_docs = html_splitter.create_documents([html_text])
html_docs
```

<CodeOutputBlock lang="python">

```
    [Document(page_content='<!DOCTYPE html>\n<html>\n    <head>', metadata={}),
     Document(page_content='<title>🦜️🔗 LangChain</title>\n        <style>', metadata={}),
     Document(page_content='body {', metadata={}),
     Document(page_content='font-family: Arial, sans-serif;', metadata={}),
     Document(page_content='}\n            h1 {', metadata={}),
     Document(page_content='color: darkblue;\n            }', metadata={}),
     Document(page_content='</style>\n    </head>\n    <body>\n        <div>', metadata={}),
     Document(page_content='<h1>🦜️🔗 LangChain</h1>', metadata={}),
     Document(page_content='<p>⚡ Building applications with LLMs through', metadata={}),
     Document(page_content='composability ⚡</p>', metadata={}),
     Document(page_content='</div>\n        <div>', metadata={}),
     Document(page_content='As an open source project in a rapidly', metadata={}),
     Document(page_content='developing field, we are extremely open to contributions.', metadata={}),
     Document(page_content='</div>\n    </body>\n</html>', metadata={})]
```

</CodeOutputBlock>


## Solidity

这里是使用 Solidity 文本分割器的示例

```python
SOL_CODE = """
pragma solidity ^0.8.20;
contract HelloWorld {
   function add(uint a, uint b) pure public returns(uint) {
       return a + b;
   }
}
"""

sol_splitter = RecursiveCharacterTextSplitter.from_language(
    language=Language.SOL, chunk_size=128, chunk_overlap=0
)
sol_docs = sol_splitter.create_documents([SOL_CODE])
sol_docs
```

<CodeOutputBlock>

```
[
    Document(page_content='pragma solidity ^0.8.20;', metadata={}),
    Document(page_content='contract HelloWorld {\n   function add(uint a, uint b) pure public returns(uint) {\n       return a + b;\n   }\n}', metadata={})
]
 ```

 </CodeOutputBlock>